system

Abstract

We provide the system. [Solution] An online platform for matching businesses in rural areas with specialized engineers concentrated in urban areas, A means of receiving and storing registration information of businesses and technical experts in a database, A means including a generative AI model that receives input of consultation content from businesses and analyzes said consultation content, A matching means that automatically selects the most suitable expert based on the analysis results of the generated AI model, A means to support the selected expert technician in providing advice to the client, A means of collecting feedback from clients and using it to improve the system, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , , , ,

[0001] The technology of the present disclosure relates to a system."

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance."

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Business operators located in local areas tend to have their efficient business operations and growth hindered due to lack of business skills and expertise. On the other hand, in urban areas, there is a concentration of engineers with advanced business skills and expertise, and there is a problem that these skills and knowledge are not sufficiently transmitted to local areas. Furthermore, there is also a risk that local business operators may encounter inappropriate consultants or fraud. Under such circumstances, it is required to enable local business operators to efficiently utilize the expertise in urban areas."

Means for Solving the Problems

[0005] The present invention provides a system for matching local businesses with urban-based technical experts via an online platform. Specifically, it includes means for receiving and storing registration information of businesses and technical experts in a database, means including a generative AI model for receiving and analyzing inquiries from businesses, matching means for automatically selecting suitable technical experts based on the analysis results, means for the selected technical experts to provide advice to local businesses, and means for collecting feedback and using it to improve the system, thereby enabling local businesses to acquire expertise safely and efficiently.

[0006] An "online platform" is a system that provides services and functions accessed via the internet, enabling information exchange and service provision among users.

[0007] A "business operator" refers to an individual or organization that engages in specific business activities, carrying out economic activities through the provision of goods or services.

[0008] A "specialized engineer" is an individual who possesses advanced knowledge and skills in a specific field and uses them to solve problems and provide support.

[0009] A "generative AI model" is a computational model that uses artificial intelligence technology to analyze data and generate new insights and results.

[0010] A "matching method" is a method or process for automatically selecting an appropriate combination based on specified conditions or criteria.

[0011] A "database" is a collection of information, a system with an organized structure for a specific purpose, designed to efficiently store, retrieve, and manage data.

[0012] "Feedback" refers to evaluations and opinions on activities carried out or services provided, and is information that helps improve results and identify challenges. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides an online platform that enables local businesses to receive knowledge and support from urban-based technical experts. Users access the platform using a terminal and register by entering their information. The registration information is stored in a database by the server. This allows for the management of profiles for both businesses and technical experts.

[0035] When entering the consultation details, the user inputs specific challenges and necessary support related to their business using their device. The server receives this information, formats it appropriately, and saves it. This consultation content is then analyzed by a generative AI model to identify the business background, the challenges it faces, and the required expertise.

[0036] The server performs appropriate matching based on the analysis results and the information of engineers managed in the database. The engineer who best matches the requirements is selected, and a notification is sent to that engineer. This notification allows the engineer to access the service provider in need of support via their terminal and provide the necessary advice and information.

[0037] Furthermore, local businesses that receive advice provide feedback via their terminals, and the server stores this feedback in a database. This feedback is used to improve the system and is also used as part of a process to pass that feedback back to engineers and other businesses.

[0038] As a concrete example, consider a case where a local agricultural business is looking for a new sales strategy. The business inputs its challenges using a terminal, and a server analyzes the situation and selects a specialist in sales strategies. This specialist advises the business on specific sales channels and marketing methods, supporting the agricultural business in developing new markets. In this way, a system is created that allows local businesses to leverage urban expertise for actual business expansion.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user uses their device to access the online platform's registration page and enters information such as their name, contact information, area of ​​expertise, and required support. The server receives the registration information submitted by the user and stores it in its database.

[0042] Step 2:

[0043] The user uses a terminal to input specific details of their inquiry. For example, they might describe in detail that they "need a new marketing strategy." The server receives the input inquiry, formats it appropriately, and saves it to the database.

[0044] Step 3:

[0045] The server uses a generative AI model to analyze the stored consultation content. This analysis identifies the background, issues, and required skills of the consultation. The generative AI model derives conclusions such as "the development of a marketing strategy is needed."

[0046] Step 4:

[0047] The server matches the information of engineers in the database based on the analysis results of the generated AI model. Using a matching algorithm, it automatically selects the most suitable engineer. For example, it might identify an engineer with expertise in marketing.

[0048] Step 5:

[0049] The server sends a notification to the selected technician. The technician accesses the platform from their terminal and prepares to respond to the designated service provider.

[0050] Step 6:

[0051] Technicians use terminals to contact businesses and provide specific advice. This includes information exchange via video calls and messaging systems. Users (businesses) then use this information to adjust and improve their businesses.

[0052] Step 7:

[0053] Users (businesses) input feedback via a terminal after receiving advice. The server receives this feedback and stores it in a database. The feedback information is used to improve the system and evaluate engineers.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] Businesses in rural areas face the challenge of not being able to quickly and effectively receive knowledge and support from specialists concentrated in urban areas. In particular, the difficulty in accessing appropriate specialists and accurately understanding the nature of their consultations leads to delays in the information and advice necessary for business development.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for receiving registration information of businesses and expert technicians and storing it in an information storage means, means for receiving and inputting consultation content from businesses and analyzing it using a generated AI model, and means for automatically selecting and coordinating a suitable expert technician based on the analysis results. This makes it possible for local businesses to quickly receive expert advice from the most suitable technician.

[0059] "Information storage means" refers to a device or system that has the function of receiving and storing data, including registration information of businesses and technical experts.

[0060] A "generative AI model" is an artificial intelligence algorithm that analyzes input data to identify the background and challenges of a business.

[0061] "Adjustment means" refers to a device or system that has the function of selecting a suitable expert based on the results analyzed by a generative AI model and automatically matching them with a business operator.

[0062] A "means of collecting opinions" refers to a system for receiving, storing, and using feedback and opinions from those seeking advice to improve the system later on.

[0063] The "notification function" is a communication method used to inform the system-selected expert technicians about the timing and content of advice to be provided.

[0064] This invention relates to the construction of an online platform that enables local businesses to receive appropriate support from expert technicians. The server uses information storage means to store registration information received from businesses and expert technicians in a database. This database manages the profiles of businesses and technicians and provides the basis for matching them.

[0065] Users access the platform using their devices and input their inquiry details. This includes information about specific business challenges and the support they need. The server receives this input and analyzes the data using a generative AI model. The generative AI model understands the inquiry and uses prompts to identify the business context, the challenges faced, and the technologies required.

[0066] Based on the analyzed data, the server automatically selects the appropriate expert technician and notifies the service provider of the selection result through a coordination mechanism. The technician receives the notification and can then provide advice directly to the service provider via a terminal. The service provider also inputs feedback on the advice received via the terminal, and the server stores this information in a data storage system. This feedback is used to improve the system and facilitate future support.

[0067] As a concrete example, consider a case where a local agricultural business is exploring new sales strategies. The business inputs its market development challenges into the platform via a terminal. The server performs analysis and automatically selects a technician with expertise in sales strategies. The selected technician proposes effective marketing methods to the agricultural business and helps it enter new markets. This allows the business to expand its business by leveraging advanced knowledge from urban areas.

[0068] An example of a prompt might be a question like, "What marketing methods would be effective for agricultural businesses exploring new sales strategies?"

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] Users access the online platform using their devices and enter the necessary personal and business information. Once the data entered by the user is transmitted, the server receives this information and stores it in a database using an information storage mechanism. This registers the user's profile in the system.

[0072] Step 2:

[0073] Users use a terminal to enter detailed information about their business needs. For example, they might enter questions about specific technical issues or business strategies. This information is organized into prompts and sent to the server. The server receives this information and records it in its database.

[0074] Step 3:

[0075] The server passes the consultation details stored in the database to the generating AI model. The generating AI model analyzes the input data and performs data calculations to identify the business background, challenges, and required expertise. The analysis results are returned to the server.

[0076] Step 4:

[0077] The server compares the analysis results obtained by the generated AI model with the information of engineers in the database. Using this information, the server executes a process to select the most suitable expert engineer. The server then verifies the profile of the selected engineer.

[0078] Step 5:

[0079] The server uses a notification function to send a message to the selected technician requesting their advice on the matter. Upon receiving the notification, the technician contacts the service provider via their terminal and prepares to provide specific advice and technical support.

[0080] Step 6:

[0081] After receiving advice from a technician, the user enters feedback on the content into their terminal. This feedback data is collected by the server and stored in an information storage system. This data is used for system improvement and sharing with other users.

[0082] (Application Example 1)

[0083] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] Local businesses often have limited access to specialized technologies and services available in urban areas. As a result, they may not receive sufficient support, particularly in designing new sales channels and developing promotional strategies utilizing virtual stores, which can make business expansion and market development difficult.

[0085] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0086] This invention includes a server that receives registration information of businesses and expert technicians and stores it in a data structure; a means that receives input of consultation content from businesses and includes a generative AI model that analyzes said consultation content; a matching means that automatically selects the most suitable expert technician; and a means that allows businesses to receive advice from experts on virtual sales channel design or promotion strategies. This enables local businesses to receive the necessary support from expert technicians and expand their businesses through the effective use of virtual stores.

[0087] An "online platform" is a system that connects local businesses with urban-based technical experts via the internet.

[0088] A "data structure" is a formatted information body used to organize and store registration information of businesses and technical experts.

[0089] A "generative AI model" is a model that uses artificial intelligence technology to analyze the content of inquiries from businesses and identify the background, challenges, and necessary skills.

[0090] The "matching method" is a function that automatically selects the most suitable expert for a business based on the analysis results of the generated AI model.

[0091] "Virtual sales channel design or promotion strategy" is a concept that uses digital technology to build sales channels and advertising methods for products and services in a virtual space.

[0092] This invention provides an online platform for efficiently matching local businesses with urban-based technical experts. The main processing is performed on a server. Specific embodiments are described below.

[0093] The hardware can include smartphones, smart glasses, or head-mounted displays. These devices are used as terminals to allow users (businesses) to access the platform. Registration information and consultation details are sent from the terminal to the server. The server receives this data and stores it in a data structure.

[0094] The AI ​​model installed on the server analyzes the inquiries received from businesses to identify the business background, challenges, and required skills. Based on this analysis, the most suitable expert is automatically selected, and the selected expert is notified. The expert can then provide corresponding advice online.

[0095] A new feature related to virtual stores is the ability to receive expert advice, which is particularly useful in designing sales channels and developing promotional strategies. For example, businesses can get specific advice from experts on how to design new digital sales channels for their specialty products.

[0096] In this invention, a practical example is to consider a case where an agricultural business operator wants to improve their sales strategy on an online marketplace. In this case, the business operator inputs the problem from a terminal. Next, the generating AI model generates optimal advice using prompt sentences such as, "Please propose a promotional strategy for a local agricultural business operator to effectively run a large-scale social media campaign. We are especially looking for creative ideas that will help in developing new markets." This enables local businesses to effectively utilize urban expertise and develop new markets.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] Users access the platform using their devices and enter their registration information and consultation details. The entered registration information and consultation details are sent to the server in digital format. This generates a data stream based on the consultation content.

[0100] Step 2:

[0101] The server receives the data stream and stores the registration information in a data structure. The consultation content is also passed to a generative AI model, which analyzes the business background and challenges through natural language processing. This analysis identifies the necessary expertise and support needs.

[0102] Step 3:

[0103] The analysis results from the generated AI model are input into the matching algorithm. The server compares the analysis results with expert engineer information in the database and automatically selects the most suitable engineer. This process obtains the expert engineer's identification data.

[0104] Step 4:

[0105] The server sends a notification to the selected specialist technician. The notification includes the details of the business's consultation and the support required, providing the technician with the basic information needed to prepare specific advice.

[0106] Step 5:

[0107] Technicians provide advice to clients via their devices. The advice is sent to the service provider in digital format and displayed on the user's device. This allows the service provider to receive feedback from expert technicians.

[0108] Step 6:

[0109] Based on the advice received, users provide feedback from their devices. This feedback is sent to the server and stored in a data structure. This information is used for future analysis and system improvements.

[0110] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0111] This invention incorporates an emotion engine that recognizes user emotions into an online platform that efficiently matches local businesses with urban-based professional technicians. The system begins with the server receiving registration information of businesses and technicians from terminals and storing it in a database. Subsequently, when businesses input consultation details using the terminal, the emotion engine analyzes the input data and evaluates the user's emotional state.

[0112] The server analyzes inquiries from businesses using a generation AI model to identify the business background, challenges, and required skills. This analysis, along with emotional information obtained from an emotion engine, is used to design a screen interface that minimizes user stress.

[0113] Furthermore, based on these analysis results, the server automatically selects the most suitable expert technician. In the selection process, emotional data obtained by the emotion engine is used as a factor in determining which technician will provide the appropriate response. In particular, if the user is experiencing stress, a technician with the ability to help them relax may be selected.

[0114] Using a notification function, the server sends client information to a selected technician, who then provides advice to the service provider via their terminal. The emotion engine constantly monitors the emotional states of both parties during the conversation and suggests adjusting the support content as needed.

[0115] As a concrete example, consider a manufacturing company considering the introduction of a new production technology. When the company inputs its inquiry, the emotion engine recognizes that the company is feeling anxious. Based on the generated AI model and emotion data, the server selects an experienced and calm engineer to provide support to reassure the company. In this way, more effective business support is achieved by taking the user's emotions into consideration.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] Users register on the online platform using their devices. Users enter their basic information, areas of expertise, and areas of interest, and the server receives this information and stores it in a database.

[0119] Step 2:

[0120] Users input their needs through their device, specifically describing the type of support they require. The server immediately sends the entered text to an emotion engine to analyze the user's emotions. This analysis can then measure, for example, levels of anxiety and stress.

[0121] Step 3:

[0122] The server uses a generative AI model to analyze the consultation content in detail. This analysis identifies the business background, the challenges being faced, and the necessary skills. Emotional data from the emotion engine is also taken into consideration to prepare the server to provide the most appropriate support for the user's situation.

[0123] Step 4:

[0124] The server automatically selects the most suitable expert from the database based on the analysis results. This selection process evaluates the expert's skills and the user's emotional state. If the user is experiencing anxiety, an expert with skills in relaxation will be selected.

[0125] Step 5:

[0126] The server promptly sends a notification to the selected technician. The technician uses their terminal to prepare for the user's consultation based on detailed consultation content and emotional information.

[0127] Step 6:

[0128] Engineers contact users via terminals and provide specific advice through online meetings and chats. An emotion engine constantly monitors the two-way conversation to ensure effective support tailored to the user's emotions.

[0129] Step 7:

[0130] After receiving advice, users input feedback on their impressions and the effects via their terminal. The server records this feedback in a database and uses it for future system improvements and evaluation of engineers.

[0131] (Example 2)

[0132] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0133] This solution addresses the problem of delays in business problem-solving due to the inability of local providers to effectively match with urban experts who possess the necessary specialized knowledge. It also addresses the issue of reduced effectiveness of dialogue due to the disregard of the client's emotional state.

[0134] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0135] In this invention, the server includes means for receiving registration information of providers and experts and storing it in an information aggregation device, means for analyzing the content of consultations from providers through a generating AI model, and means for matching the most suitable expert based on emotional data obtained by an emotion analysis device. This makes it possible to appropriately match the necessary expert while taking into account the emotions of the person seeking advice, thereby enabling effective problem solving.

[0136] A "provider" refers to a local organization or individual that provides services or goods.

[0137] A "specialist" is an individual or group located in an urban area who possesses advanced knowledge or skills in a specific field.

[0138] An "online platform" is online software that connects providers and experts via the internet.

[0139] An "information aggregation device" refers to a database or storage device that stores and manages registration information of providers and experts.

[0140] A "generative AI model" is a model built with artificial intelligence that analyzes input data to identify the background, problems, and required capabilities of a task.

[0141] An "emotion analysis device" is a system that analyzes the input data of a provider and evaluates their emotional state.

[0142] "Matching method" refers to the process or system for selecting the most suitable expert based on the generated AI model and sentiment analysis results.

[0143] The following describes embodiments for carrying out the present invention. This system forms an online platform that efficiently matches local providers with urban experts.

[0144] The server receives registration information of providers and experts from terminals via the internet and stores it in an information aggregation device. Database technology is used in this process, and the information is managed securely. When a user inputs their consultation details using a terminal, a generative AI model running on the server analyzes the input data. Specifically, it utilizes natural language processing technology to identify the background of the work, the problem, and the required skills. In this process, an emotion analysis device also judges the emotional aspects of the input data and evaluates the user's emotional state.

[0145] Based on the analyzed data, the server performs a matching process to select the most suitable expert. Information is sent to the selected expert via a notification system, and the expert can use their terminal to provide advice to the provider.

[0146] As a concrete example, consider a case where a manufacturer seeks advice regarding the introduction of a new technology. Suppose the user enters the prompt message "I have concerns about the new manufacturing process" into the terminal. In this case, the generative AI model detects the provider's sources of anxiety, and the emotion analysis device evaluates their emotional tendencies. The server then selects a calm expert with relaxation skills and has them provide expert advice, thereby reassuring the provider.

[0147] In this way, users can engage in effective and stress-free interactions with experts who possess a high level of specialized knowledge.

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] Users enter their registration information as providers or professionals using a terminal. This data includes basic information such as name, contact information, skills, and experience. The terminal sends this data to a server, which stores it in an information aggregation device once the user has completed the entry. A database system is used for this storage process, ensuring security and quick access.

[0151] Step 2:

[0152] The provider inputs the consultation details via a terminal. Once the consultation details are entered in prompt format, the terminal sends the data to the server. The server inputs the received consultation details into a generating AI model and performs analysis to identify the background of the work, the problem, and the required skills. Natural language processing technology is used here, and the analyzed information is stored on the server as output.

[0153] Step 3:

[0154] In parallel, the server uses an emotion analysis device to perform an emotional analysis of the input consultation content. This uses an algorithm that evaluates the emotional elements of the text, and the user's emotional state is estimated. The result of the emotional evaluation is obtained as output and is also stored within the server.

[0155] Step 4:

[0156] Based on the analysis results and sentiment evaluation results from the generative AI model, the server executes a matching algorithm to select the most suitable expert. The input consists of both analysis information and sentiment evaluation, and considering these, it outputs a list of the most appropriate experts from among multiple candidates.

[0157] Step 5:

[0158] The server sends information to selected experts via a notification system. This information includes details about the provider's consultation topic and the required skills. Upon receiving the notification, experts use their devices to prepare to provide advice to the provider.

[0159] Step 6:

[0160] During conversations with experts via the terminal, the emotion analysis device continuously monitors the user's emotional state. In response to changes in the emotional state, the server provides real-time feedback to the expert, suggesting adjustments to the support provided to enhance the effectiveness of the conversation. The ultimate output is an improved quality of conversation.

[0161] (Application Example 2)

[0162] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0163] In today's industrial environment, field workers face the challenge of maintaining and troubleshooting complex machinery, requiring appropriate and timely technical support. However, providing nuanced support tailored to each worker's skill level and emotional state is difficult, potentially impacting work efficiency and accuracy. Against this backdrop, there is a need for a technical support platform that takes workers' emotional states into account.

[0164] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0165] In this invention, the server includes means for receiving registration information of business organizations and professional personnel and storing it in a data storage unit; means including an emotion analysis engine that recognizes the emotional state of workers and provides appropriate technical support according to that state; and means for generating simpler work instructions and technical support based on the emotional state of workers. This makes it possible for workers to receive optimal technical support quickly while reducing their emotional burden.

[0166] A "business organization" is an entity organized to perform a specific business or commercial activity.

[0167] A "specialized professional" is an individual who possesses a high level of expertise and skills in a specific field.

[0168] An "online infrastructure" is a system with a platform structure that can be accessed via the internet.

[0169] A "data storage unit" is a recording medium or technical means for centrally storing and managing information.

[0170] A "data generation model" is an algorithm or system that analyzes input information and generates new information or knowledge.

[0171] "Verification means" refers to a method or apparatus for comparing different pieces of information and determining their compatibility or relationship.

[0172] An "emotion analysis engine" is a program or device for identifying and evaluating a person's emotions.

[0173] "Technical support" refers to services that provide expertise and assistance with the operation and maintenance of machinery and equipment.

[0174] This system is designed to efficiently match local businesses with urban professionals online. The server receives registration information from businesses and professionals and stores it in a data aggregation unit. This provides a foundation for users to access the system at any time.

[0175] When a consultation request is entered into the server, it uses a data generation model to analyze the content and identify the background, problems, and required skills for the work. For example, using Google's TENSORFLOW® as the generation AI model enables rapid and accurate analysis. Based on the analysis results, the most suitable professional is automatically selected using a matching system. In this process, the emotional state of the person seeking advice is also considered using an emotion analysis engine.

[0176] The emotion analysis engine analyzes emotional data obtained from the user's smartphone camera and microphone to determine in real time whether the user is experiencing anxiety or stress. This allows for the provision of technical support tailored to the user's emotional state. Microsoft® Azure® Cognitive Services is a possible solution for emotion analysis.

[0177] For example, if a factory worker is struggling to troubleshoot a robot, the emotion analysis engine detects this, and a generative AI model generates a simple troubleshooting guide. Then, if necessary, the appropriate expert is automatically selected and contacted to provide remote technical assistance.

[0178] A concrete example of a prompt message is: "A robot malfunction has been detected, and the operator is feeling anxious. Please generate a guide to resolve the issue with simple steps." This prompt allows the system to generate a guide that provides appropriate assistance based on the situation.

[0179] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0180] Step 1:

[0181] The terminal receives registration information for business organizations and professional personnel from the input screen. The entered information is stored in a database based on each attribute (name, contact method, area of ​​expertise, etc.). This allows the server to quickly access and manage the relevant information.

[0182] Step 2:

[0183] The user inputs consultation information through their terminal. The server receives this information and sends it as a prompt to the generating AI model. The consultation content is treated as text data, and the generating AI model (using TensorFlow) analyzes the business background and problems to identify the necessary capabilities. The output is returned to the server as the analysis result.

[0184] Step 3:

[0185] The server uses an emotion analysis engine to analyze user emotion data collected from the device's camera and microphone. Using audio and video data as input, it determines the user's emotional state using Azure Cognitive Services. The output is emotional state information that allows the user to receive technical support with confidence.

[0186] Step 4:

[0187] The server selects the most suitable professional based on the analysis results from steps 2 and 3. Using business analysis results and emotional state information as input data, the server generates information on the selected professional as output using a matching mechanism. In this process, the different skill sets and emotional response capabilities of the professionals are taken into consideration.

[0188] Step 5:

[0189] Selected professionals receive immediate notifications via their terminals. These notifications include information about the organization's concerns and emotional state. Based on these notifications, the professionals provide appropriate advice to the organization to resolve the issues. The output is the advice given to the organization and its effectiveness.

[0190] Step 6:

[0191] The server collects feedback from the business organization. The feedback data received from terminals is processed for system improvement and used to enhance the accuracy of future analyses and selections. This feedback provides crucial information for optimizing the system.

[0192] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0193] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0194] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0195] [Second Embodiment]

[0196] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0197] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0198] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0199] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0200] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0201] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0202] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0203] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0204] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0205] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0206] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0207] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0208] This invention provides an online platform that enables local businesses to receive knowledge and support from urban-based technical experts. Users access the platform using a terminal and register by entering their information. The registration information is stored in a database by the server. This allows for the management of profiles for both businesses and technical experts.

[0209] When entering the consultation details, the user inputs specific challenges and necessary support related to their business using their device. The server receives this information, formats it appropriately, and saves it. This consultation content is then analyzed by a generative AI model to identify the business background, the challenges it faces, and the required expertise.

[0210] The server performs appropriate matching based on the analysis results and the information of engineers managed in the database. The engineer who best matches the requirements is selected, and a notification is sent to that engineer. This notification allows the engineer to access the service provider in need of support via their terminal and provide the necessary advice and information.

[0211] Furthermore, local businesses that receive advice provide feedback via their terminals, and the server stores this feedback in a database. This feedback is used to improve the system and is also used as part of a process to pass that feedback back to engineers and other businesses.

[0212] As a concrete example, consider a case where a local agricultural business is looking for a new sales strategy. The business inputs its challenges using a terminal, and a server analyzes the situation and selects a specialist in sales strategies. This specialist advises the business on specific sales channels and marketing methods, supporting the agricultural business in developing new markets. In this way, a system is created that allows local businesses to leverage urban expertise for actual business expansion.

[0213] The following describes the processing flow.

[0214] Step 1:

[0215] The user uses their device to access the online platform's registration page and enters information such as their name, contact information, area of ​​expertise, and required support. The server receives the registration information submitted by the user and stores it in its database.

[0216] Step 2:

[0217] The user uses a terminal to input specific details of their inquiry. For example, they might describe in detail that they "need a new marketing strategy." The server receives the input inquiry, formats it appropriately, and saves it to the database.

[0218] Step 3:

[0219] The server uses a generative AI model to analyze the stored consultation content. This analysis identifies the background, issues, and required skills of the consultation. The generative AI model derives conclusions such as "the development of a marketing strategy is needed."

[0220] Step 4:

[0221] The server matches the information of engineers in the database based on the analysis results of the generated AI model. Using a matching algorithm, it automatically selects the most suitable engineer. For example, it might identify an engineer with expertise in marketing.

[0222] Step 5:

[0223] The server sends a notification to the selected technician. The technician accesses the platform from their terminal and prepares to respond to the designated service provider.

[0224] Step 6:

[0225] Technicians use terminals to contact businesses and provide specific advice. This includes information exchange via video calls and messaging systems. Users (businesses) then use this information to adjust and improve their businesses.

[0226] Step 7:

[0227] Users (businesses) input feedback via a terminal after receiving advice. The server receives this feedback and stores it in a database. The feedback information is used to improve the system and evaluate engineers.

[0228] (Example 1)

[0229] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0230] Businesses in rural areas face the challenge of not being able to quickly and effectively receive knowledge and support from specialists concentrated in urban areas. In particular, the difficulty in accessing appropriate specialists and accurately understanding the nature of their consultations leads to delays in the information and advice necessary for business development.

[0231] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0232] In this invention, the server includes means for receiving registration information of businesses and expert technicians and storing it in an information storage means, means for receiving and inputting consultation content from businesses and analyzing it using a generated AI model, and means for automatically selecting and coordinating a suitable expert technician based on the analysis results. This makes it possible for local businesses to quickly receive expert advice from the most suitable technician.

[0233] "Information storage means" refers to a device or system that has the function of receiving and storing data, including registration information of businesses and technical experts.

[0234] A "generative AI model" is an artificial intelligence algorithm that analyzes input data to identify the background and challenges of a business.

[0235] "Adjustment means" refers to a device or system that has the function of selecting a suitable expert based on the results analyzed by a generative AI model and automatically matching them with a business operator.

[0236] A "means of collecting opinions" refers to a system for receiving, storing, and using feedback and opinions from those seeking advice to improve the system later on.

[0237] The "notification function" is a communication method used to inform the system-selected expert technicians about the timing and content of advice to be provided.

[0238] This invention relates to the construction of an online platform that enables local businesses to receive appropriate support from expert technicians. The server uses information storage means to store registration information received from businesses and expert technicians in a database. This database manages the profiles of businesses and technicians and provides the basis for matching them.

[0239] Users access the platform using their devices and input their inquiry details. This includes information about specific business challenges and the support they need. The server receives this input and analyzes the data using a generative AI model. The generative AI model understands the inquiry and uses prompts to identify the business context, the challenges faced, and the technologies required.

[0240] Based on the analyzed data, the server automatically selects the appropriate expert technician and notifies the service provider of the selection result through a coordination mechanism. The technician receives the notification and can then provide advice directly to the service provider via a terminal. The service provider also inputs feedback on the advice received via the terminal, and the server stores this information in a data storage system. This feedback is used to improve the system and facilitate future support.

[0241] As a concrete example, consider a case where a local agricultural business is exploring new sales strategies. The business inputs its market development challenges into the platform via a terminal. The server performs analysis and automatically selects a technician with expertise in sales strategies. The selected technician proposes effective marketing methods to the agricultural business and helps it enter new markets. This allows the business to expand its business by leveraging advanced knowledge from urban areas.

[0242] An example of a prompt might be a question like, "What marketing methods would be effective for agricultural businesses exploring new sales strategies?"

[0243] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0244] Step 1:

[0245] Users access the online platform using their devices and enter the necessary personal and business information. Once the data entered by the user is transmitted, the server receives this information and stores it in a database using an information storage mechanism. This registers the user's profile in the system.

[0246] Step 2:

[0247] Users use a terminal to enter detailed information about their business needs. For example, they might enter questions about specific technical issues or business strategies. This information is organized into prompts and sent to the server. The server receives this information and records it in its database.

[0248] Step 3:

[0249] The server passes the consultation details stored in the database to the generating AI model. The generating AI model analyzes the input data and performs data calculations to identify the business background, challenges, and required expertise. The analysis results are returned to the server.

[0250] Step 4:

[0251] The server compares the analysis results obtained by the generated AI model with the information of engineers in the database. Using this information, the server executes a process to select the most suitable expert engineer. The server then verifies the profile of the selected engineer.

[0252] Step 5:

[0253] The server uses a notification function to send a message to the selected technician requesting their advice on the matter. Upon receiving the notification, the technician contacts the service provider via their terminal and prepares to provide specific advice and technical support.

[0254] Step 6:

[0255] After receiving advice from a technician, the user enters feedback on the content into their terminal. This feedback data is collected by the server and stored in an information storage system. This data is used for system improvement and sharing with other users.

[0256] (Application Example 1)

[0257] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] Local businesses often have limited access to specialized technologies and services available in urban areas. As a result, they may not receive sufficient support, particularly in designing new sales channels and developing promotional strategies utilizing virtual stores, which can make business expansion and market development difficult.

[0259] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0260] This invention includes a server that receives registration information of businesses and expert technicians and stores it in a data structure; a means that receives input of consultation content from businesses and includes a generative AI model that analyzes said consultation content; a matching means that automatically selects the most suitable expert technician; and a means that allows businesses to receive advice from experts on virtual sales channel design or promotion strategies. This enables local businesses to receive the necessary support from expert technicians and expand their businesses through the effective use of virtual stores.

[0261] An "online platform" is a system that connects local businesses with urban-based technical experts via the internet.

[0262] A "data structure" is a formatted information body used to organize and store registration information of businesses and technical experts.

[0263] A "generative AI model" is a model that uses artificial intelligence technology to analyze the content of inquiries from businesses and identify the background, challenges, and necessary skills.

[0264] The "matching method" is a function that automatically selects the most suitable expert for a business based on the analysis results of the generated AI model.

[0265] "Virtual sales channel design or promotion strategy" is a concept that uses digital technology to build sales channels and advertising methods for products and services in a virtual space.

[0266] This invention provides an online platform for efficiently matching local businesses with urban-based technical experts. The main processing is performed on a server. Specific embodiments are described below.

[0267] The hardware can include smartphones, smart glasses, or head-mounted displays. These devices are used as terminals to allow users (businesses) to access the platform. Registration information and consultation details are sent from the terminal to the server. The server receives this data and stores it in a data structure.

[0268] The AI ​​model installed on the server analyzes the inquiries received from businesses to identify the business background, challenges, and required skills. Based on this analysis, the most suitable expert is automatically selected, and the selected expert is notified. The expert can then provide corresponding advice online.

[0269] A new feature related to virtual stores is the ability to receive expert advice, which is particularly useful in designing sales channels and developing promotional strategies. For example, businesses can get specific advice from experts on how to design new digital sales channels for their specialty products.

[0270] In this invention, a practical example is to consider a case where an agricultural business operator wants to improve their sales strategy on an online marketplace. In this case, the business operator inputs the problem from a terminal. Next, the generating AI model generates optimal advice using prompt sentences such as, "Please propose a promotional strategy for a local agricultural business operator to effectively run a large-scale social media campaign. We are especially looking for creative ideas that will help in developing new markets." This enables local businesses to effectively utilize urban expertise and develop new markets.

[0271] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0272] Step 1:

[0273] Users access the platform using their devices and enter their registration information and consultation details. The entered registration information and consultation details are sent to the server in digital format. This generates a data stream based on the consultation content.

[0274] Step 2:

[0275] The server receives the data stream and stores the registration information in a data structure. The consultation content is also passed to a generative AI model, which analyzes the business background and challenges through natural language processing. This analysis identifies the necessary expertise and support needs.

[0276] Step 3:

[0277] The analysis results from the generated AI model are input into the matching algorithm. The server compares the analysis results with expert engineer information in the database and automatically selects the most suitable engineer. This process obtains the expert engineer's identification data.

[0278] Step 4:

[0279] The server sends a notification to the selected specialist technician. The notification includes the details of the business's consultation and the support required, providing the technician with the basic information needed to prepare specific advice.

[0280] Step 5:

[0281] Technicians provide advice to clients via their devices. The advice is sent to the service provider in digital format and displayed on the user's device. This allows the service provider to receive feedback from expert technicians.

[0282] Step 6:

[0283] Based on the received advice, the user provides feedback from the terminal. This feedback is sent to the server and stored in the data structure. This information is used for future analysis and system improvement.

[0284] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0285] The present invention incorporates an emotion engine for recognizing the user's emotion into an online platform that efficiently matches local operators and urban professionals. This system starts with receiving the registration information of the operator and the professional from the terminal and storing it in the database by the server. Subsequently, when the operator inputs the consultation content using the terminal, the emotion engine analyzes the input data and evaluates the user's emotional state.

[0286] The server analyzes the consultation content from the operator by the generation AI model to identify the background, issues, and required skills in the business. In addition to this analysis information, using the emotion information obtained from the emotion engine, the screen interface is designed in a state where the user feels the least stress.

[0287] Furthermore, based on this analysis result, the server automatically selects the optimal professional. In the selection process, the emotion data obtained by the emotion engine is used as a factor for determining which technician will provide an appropriate response. In particular, when the user is feeling stressed, a technician with the ability to relax may be selected. <了

[0288] Using a notification function, the server sends client information to a selected technician, who then provides advice to the service provider via their terminal. The emotion engine constantly monitors the emotional states of both parties during the conversation and suggests adjusting the support content as needed.

[0289] As a concrete example, consider a manufacturing company considering the introduction of a new production technology. When the company inputs its inquiry, the emotion engine recognizes that the company is feeling anxious. Based on the generated AI model and emotion data, the server selects an experienced and calm engineer to provide support to reassure the company. In this way, more effective business support is achieved by taking the user's emotions into consideration.

[0290] The following describes the processing flow.

[0291] Step 1:

[0292] Users register on the online platform using their devices. Users enter their basic information, areas of expertise, and areas of interest, and the server receives this information and stores it in a database.

[0293] Step 2:

[0294] Users input their needs through their device, specifically describing the type of support they require. The server immediately sends the entered text to an emotion engine to analyze the user's emotions. This analysis can then measure, for example, levels of anxiety and stress.

[0295] Step 3:

[0296] The server uses a generative AI model to analyze the consultation content in detail. This analysis identifies the business background, the challenges being faced, and the necessary skills. Emotional data from the emotion engine is also taken into consideration to prepare the server to provide the most appropriate support for the user's situation.

[0297] Step 4:

[0298] Based on the analysis results, the server automatically selects the most suitable expert from the database. In this selection, the skills of the engineer and the emotional state of the user are evaluated. If the user is feeling anxious, those with skills to make the user relax are especially selected.

[0299] Step 5:

[0300] The server promptly sends a notification to the selected engineer. The engineer uses the terminal to prepare for the user based on the detailed consultation content and emotional information.

[0301] [[ID=1X]] Step 6:

[0302] The engineer contacts the user through the terminal and provides specific advice through online meetings, chats, etc. The emotion engine constantly monitors the two-way conversation and supports to ensure effective support according to the user's emotions.

[0303] Step 7:

[0304] After receiving the advice, the user inputs feedback on their feelings and effects from the terminal. The server records this feedback in the database and uses it for future system improvement and engineer evaluation.

[0305] (Example 2)

[0306] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0307] Providers in local areas cannot effectively match with experts in urban areas who have the necessary expertise, resulting in delays in solving business problems. Also, the problem that the effectiveness of the conversation decreases due to ignoring the emotional state of the counselor is also an issue.

[0308] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0309] In this invention, the server includes means for receiving registration information of providers and experts and storing it in an information aggregation device, means for analyzing the content of consultations from providers through a generating AI model, and means for matching the most suitable expert based on emotional data obtained by an emotion analysis device. This makes it possible to appropriately match the necessary expert while taking into account the emotions of the person seeking advice, thereby enabling effective problem solving.

[0310] A "provider" refers to a local organization or individual that provides services or goods.

[0311] A "specialist" is an individual or group located in an urban area who possesses advanced knowledge or skills in a specific field.

[0312] An "online platform" is online software that connects providers and experts via the internet.

[0313] An "information aggregation device" refers to a database or storage device that stores and manages registration information of providers and experts.

[0314] A "generative AI model" is a model built with artificial intelligence that analyzes input data to identify the background, problems, and required capabilities of a task.

[0315] An "emotion analysis device" is a system that analyzes the input data of a provider and evaluates their emotional state.

[0316] "Matching method" refers to the process or system for selecting the most suitable expert based on the generated AI model and sentiment analysis results.

[0317] The following describes embodiments for carrying out the present invention. This system forms an online platform that efficiently matches local providers with urban experts.

[0318] The server receives registration information of providers and experts from terminals via the internet and stores it in an information aggregation device. Database technology is used in this process, and the information is managed securely. When a user inputs their consultation details using a terminal, a generative AI model running on the server analyzes the input data. Specifically, it utilizes natural language processing technology to identify the background of the work, the problem, and the required skills. In this process, an emotion analysis device also judges the emotional aspects of the input data and evaluates the user's emotional state.

[0319] Based on the analyzed data, the server performs a matching process to select the most suitable expert. Information is sent to the selected expert via a notification system, and the expert can use their terminal to provide advice to the provider.

[0320] As a concrete example, consider a case where a manufacturer seeks advice regarding the introduction of a new technology. Suppose the user enters the prompt message "I have concerns about the new manufacturing process" into the terminal. In this case, the generative AI model detects the provider's sources of anxiety, and the emotion analysis device evaluates their emotional tendencies. The server then selects a calm expert with relaxation skills and has them provide expert advice, thereby reassuring the provider.

[0321] In this way, users can engage in effective and stress-free interactions with experts who possess a high level of specialized knowledge.

[0322] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0323] Step 1:

[0324] Users enter their registration information as providers or professionals using a terminal. This data includes basic information such as name, contact information, skills, and experience. The terminal sends this data to a server, which stores it in an information aggregation device once the user has completed the entry. A database system is used for this storage process, ensuring security and quick access.

[0325] Step 2:

[0326] The provider inputs the consultation details via a terminal. Once the consultation details are entered in prompt format, the terminal sends the data to the server. The server inputs the received consultation details into a generating AI model and performs analysis to identify the background of the work, the problem, and the required skills. Natural language processing technology is used here, and the analyzed information is stored on the server as output.

[0327] Step 3:

[0328] In parallel, the server uses an emotion analysis device to perform an emotional analysis of the input consultation content. This uses an algorithm that evaluates the emotional elements of the text, and the user's emotional state is estimated. The result of the emotional evaluation is obtained as output and is also stored within the server.

[0329] Step 4:

[0330] Based on the analysis results and sentiment evaluation results from the generative AI model, the server executes a matching algorithm to select the most suitable expert. The input consists of both analysis information and sentiment evaluation, and considering these, it outputs a list of the most appropriate experts from among multiple candidates.

[0331] Step 5:

[0332] The server sends information to selected experts via a notification system. This information includes details about the provider's consultation topic and the required skills. Upon receiving the notification, experts use their devices to prepare to provide advice to the provider.

[0333] Step 6:

[0334] During conversations with experts via the terminal, the emotion analysis device continuously monitors the user's emotional state. In response to changes in the emotional state, the server provides real-time feedback to the expert, suggesting adjustments to the support provided to enhance the effectiveness of the conversation. The ultimate output is an improved quality of conversation.

[0335] (Application Example 2)

[0336] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0337] In today's industrial environment, field workers face the challenge of maintaining and troubleshooting complex machinery, requiring appropriate and timely technical support. However, providing nuanced support tailored to each worker's skill level and emotional state is difficult, potentially impacting work efficiency and accuracy. Against this backdrop, there is a need for a technical support platform that takes workers' emotional states into account.

[0338] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0339] In this invention, the server includes means for receiving registration information of business organizations and professional personnel and storing it in a data storage unit; means including an emotion analysis engine that recognizes the emotional state of workers and provides appropriate technical support according to that state; and means for generating simpler work instructions and technical support based on the emotional state of workers. This makes it possible for workers to receive optimal technical support quickly while reducing their emotional burden.

[0340] A "business organization" is an entity organized to perform a specific business or commercial activity.

[0341] A "specialized professional" is an individual who possesses a high level of expertise and skills in a specific field.

[0342] An "online infrastructure" is a system with a platform structure that can be accessed via the internet.

[0343] A "data storage unit" is a recording medium or technical means for centrally storing and managing information.

[0344] A "data generation model" is an algorithm or system that analyzes input information and generates new information or knowledge.

[0345] "Verification means" refers to a method or apparatus for comparing different pieces of information and determining their compatibility or relationship.

[0346] An "emotion analysis engine" is a program or device for identifying and evaluating a person's emotions.

[0347] "Technical support" refers to services that provide expertise and assistance with the operation and maintenance of machinery and equipment.

[0348] This system is designed to efficiently match local businesses with urban professionals online. The server receives registration information from businesses and professionals and stores it in a data aggregation unit. This provides a foundation for users to access the system at any time.

[0349] When a consultation request is entered into the server, it uses a data generation model to analyze the content and identify the background, problems, and required skills for the work. Using a generation AI model such as Google's TensorFlow enables rapid and accurate analysis. Based on the analysis results, the most suitable professional is automatically selected using matching methods. In this process, the emotional state of the person seeking advice is also considered using an emotion analysis engine.

[0350] The emotion analysis engine analyzes emotional data obtained from the user's smartphone camera and microphone to determine in real time whether the user is experiencing anxiety or stress. This allows for the provision of technical support tailored to the user's emotional state. Microsoft's Azure Cognitive Services could be used for emotion analysis.

[0351] For example, if a factory worker is struggling to troubleshoot a robot, the emotion analysis engine detects this, and a generative AI model generates a simple troubleshooting guide. Then, if necessary, the appropriate expert is automatically selected and contacted to provide remote technical assistance.

[0352] A concrete example of a prompt message is: "A robot malfunction has been detected, and the operator is feeling anxious. Please generate a guide to resolve the issue with simple steps." This prompt allows the system to generate a guide that provides appropriate assistance based on the situation.

[0353] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0354] Step 1:

[0355] The terminal receives registration information for business organizations and professional personnel from the input screen. The entered information is stored in a database based on each attribute (name, contact method, area of ​​expertise, etc.). This allows the server to quickly access and manage the relevant information.

[0356] Step 2:

[0357] The user inputs consultation information through their terminal. The server receives this information and sends it as a prompt to the generating AI model. The consultation content is treated as text data, and the generating AI model (using TensorFlow) analyzes the business background and problems to identify the necessary capabilities. The output is returned to the server as the analysis result.

[0358] Step 3:

[0359] The server uses an emotion analysis engine to analyze user emotion data collected from the device's camera and microphone. Using audio and video data as input, it determines the user's emotional state using Azure Cognitive Services. The output is emotional state information that allows the user to receive technical support with confidence.

[0360] Step 4:

[0361] The server selects the most suitable professional based on the analysis results from steps 2 and 3. Using business analysis results and emotional state information as input data, the server generates information on the selected professional as output using a matching mechanism. In this process, the different skill sets and emotional response capabilities of the professionals are taken into consideration.

[0362] Step 5:

[0363] Selected professionals receive immediate notifications via their terminals. These notifications include information about the organization's concerns and emotional state. Based on these notifications, the professionals provide appropriate advice to the organization to resolve the issues. The output is the advice given to the organization and its effectiveness.

[0364] Step 6:

[0365] The server collects feedback from the business organization. The feedback data received from terminals is processed for system improvement and used to enhance the accuracy of future analyses and selections. This feedback provides crucial information for optimizing the system.

[0366] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0367] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0368] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0369] [Third Embodiment]

[0370] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0371] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0372] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0373] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0374] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0375] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0376] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0377] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0378] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0379] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0380] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0381] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0382] This invention provides an online platform that enables local businesses to receive knowledge and support from urban-based technical experts. Users access the platform using a terminal and register by entering their information. The registration information is stored in a database by the server. This allows for the management of profiles for both businesses and technical experts.

[0383] When entering the consultation details, the user inputs specific challenges and necessary support related to their business using their device. The server receives this information, formats it appropriately, and saves it. This consultation content is then analyzed by a generative AI model to identify the business background, the challenges it faces, and the required expertise.

[0384] The server performs appropriate matching based on the analysis results and the information of engineers managed in the database. The engineer who best matches the requirements is selected, and a notification is sent to that engineer. This notification allows the engineer to access the service provider in need of support via their terminal and provide the necessary advice and information.

[0385] Furthermore, local businesses that receive advice provide feedback via their terminals, and the server stores this feedback in a database. This feedback is used to improve the system and is also used as part of a process to pass that feedback back to engineers and other businesses.

[0386] As a concrete example, consider a case where a local agricultural business is looking for a new sales strategy. The business inputs its challenges using a terminal, and a server analyzes the situation and selects a specialist in sales strategies. This specialist advises the business on specific sales channels and marketing methods, supporting the agricultural business in developing new markets. In this way, a system is created that allows local businesses to leverage urban expertise for actual business expansion.

[0387] The following describes the processing flow.

[0388] Step 1:

[0389] The user uses their device to access the online platform's registration page and enters information such as their name, contact information, area of ​​expertise, and required support. The server receives the registration information submitted by the user and stores it in its database.

[0390] Step 2:

[0391] The user uses a terminal to input specific details of their inquiry. For example, they might describe in detail that they "need a new marketing strategy." The server receives the input inquiry, formats it appropriately, and saves it to the database.

[0392] Step 3:

[0393] The server uses a generative AI model to analyze the stored consultation content. This analysis identifies the background, issues, and required skills of the consultation. The generative AI model derives conclusions such as "the development of a marketing strategy is needed."

[0394] Step 4:

[0395] The server matches the information of engineers in the database based on the analysis results of the generated AI model. Using a matching algorithm, it automatically selects the most suitable engineer. For example, it might identify an engineer with expertise in marketing.

[0396] Step 5:

[0397] The server sends a notification to the selected technician. The technician accesses the platform from their terminal and prepares to respond to the designated service provider.

[0398] Step 6:

[0399] Technicians use terminals to contact businesses and provide specific advice. This includes information exchange via video calls and messaging systems. Users (businesses) then use this information to adjust and improve their businesses.

[0400] Step 7:

[0401] Users (businesses) input feedback via a terminal after receiving advice. The server receives this feedback and stores it in a database. The feedback information is used to improve the system and evaluate engineers.

[0402] (Example 1)

[0403] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0404] Businesses in rural areas face the challenge of not being able to quickly and effectively receive knowledge and support from specialists concentrated in urban areas. In particular, the difficulty in accessing appropriate specialists and accurately understanding the nature of their consultations leads to delays in the information and advice necessary for business development.

[0405] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0406] In this invention, the server includes means for receiving registration information of businesses and expert technicians and storing it in an information storage means, means for receiving and inputting consultation content from businesses and analyzing it using a generated AI model, and means for automatically selecting and coordinating a suitable expert technician based on the analysis results. This makes it possible for local businesses to quickly receive expert advice from the most suitable technician.

[0407] "Information storage means" refers to a device or system that has the function of receiving and storing data, including registration information of businesses and technical experts.

[0408] A "generative AI model" is an artificial intelligence algorithm that analyzes input data to identify the background and challenges of a business.

[0409] "Adjustment means" refers to a device or system that has the function of selecting a suitable expert based on the results analyzed by a generative AI model and automatically matching them with a business operator.

[0410] A "means of collecting opinions" refers to a system for receiving, storing, and using feedback and opinions from those seeking advice to improve the system later on.

[0411] The "notification function" is a communication method used to inform the system-selected expert technicians about the timing and content of advice to be provided.

[0412] This invention relates to the construction of an online platform that enables local businesses to receive appropriate support from expert technicians. The server uses information storage means to store registration information received from businesses and expert technicians in a database. This database manages the profiles of businesses and technicians and provides the basis for matching them.

[0413] Users access the platform using their devices and input their inquiry details. This includes information about specific business challenges and the support they need. The server receives this input and analyzes the data using a generative AI model. The generative AI model understands the inquiry and uses prompts to identify the business context, the challenges faced, and the technologies required.

[0414] Based on the analyzed data, the server automatically selects the appropriate expert technician and notifies the service provider of the selection result through a coordination mechanism. The technician receives the notification and can then provide advice directly to the service provider via a terminal. The service provider also inputs feedback on the advice received via the terminal, and the server stores this information in a data storage system. This feedback is used to improve the system and facilitate future support.

[0415] As a concrete example, consider a case where a local agricultural business is exploring new sales strategies. The business inputs its market development challenges into the platform via a terminal. The server performs analysis and automatically selects a technician with expertise in sales strategies. The selected technician proposes effective marketing methods to the agricultural business and helps it enter new markets. This allows the business to expand its business by leveraging advanced knowledge from urban areas.

[0416] An example of a prompt might be a question like, "What marketing methods would be effective for agricultural businesses exploring new sales strategies?"

[0417] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0418] Step 1:

[0419] Users access the online platform using their devices and enter the necessary personal and business information. Once the data entered by the user is transmitted, the server receives this information and stores it in a database using an information storage mechanism. This registers the user's profile in the system.

[0420] Step 2:

[0421] Users use a terminal to enter detailed information about their business needs. For example, they might enter questions about specific technical issues or business strategies. This information is organized into prompts and sent to the server. The server receives this information and records it in its database.

[0422] Step 3:

[0423] The server passes the consultation details stored in the database to the generating AI model. The generating AI model analyzes the input data and performs data calculations to identify the business background, challenges, and required expertise. The analysis results are returned to the server.

[0424] Step 4:

[0425] The server compares the analysis results obtained by the generated AI model with the information of engineers in the database. Using this information, the server executes a process to select the most suitable expert engineer. The server then verifies the profile of the selected engineer.

[0426] Step 5:

[0427] The server uses a notification function to send a message to the selected technician requesting their advice on the matter. Upon receiving the notification, the technician contacts the service provider via their terminal and prepares to provide specific advice and technical support.

[0428] Step 6:

[0429] After receiving advice from a technician, the user enters feedback on the content into their terminal. This feedback data is collected by the server and stored in an information storage system. This data is used for system improvement and sharing with other users.

[0430] (Application Example 1)

[0431] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0432] Local businesses often have limited access to specialized technologies and services available in urban areas. As a result, they may not receive sufficient support, particularly in designing new sales channels and developing promotional strategies utilizing virtual stores, which can make business expansion and market development difficult.

[0433] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0434] This invention includes a server that receives registration information of businesses and expert technicians and stores it in a data structure; a means that receives input of consultation content from businesses and includes a generative AI model that analyzes said consultation content; a matching means that automatically selects the most suitable expert technician; and a means that allows businesses to receive advice from experts on virtual sales channel design or promotion strategies. This enables local businesses to receive the necessary support from expert technicians and expand their businesses through the effective use of virtual stores.

[0435] An "online platform" is a system that connects local businesses with urban-based technical experts via the internet.

[0436] A "data structure" is a formatted information body used to organize and store registration information of businesses and technical experts.

[0437] A "generative AI model" is a model that uses artificial intelligence technology to analyze the content of inquiries from businesses and identify the background, challenges, and necessary skills.

[0438] The "matching method" is a function that automatically selects the most suitable expert for a business based on the analysis results of the generated AI model.

[0439] "Virtual sales channel design or promotion strategy" is a concept that uses digital technology to build sales channels and advertising methods for products and services in a virtual space.

[0440] This invention provides an online platform for efficiently matching local businesses with urban-based technical experts. The main processing is performed on a server. Specific embodiments are described below.

[0441] The hardware can include smartphones, smart glasses, or head-mounted displays. These devices are used as terminals to allow users (businesses) to access the platform. Registration information and consultation details are sent from the terminal to the server. The server receives this data and stores it in a data structure.

[0442] The AI ​​model installed on the server analyzes the inquiries received from businesses to identify the business background, challenges, and required skills. Based on this analysis, the most suitable expert is automatically selected, and the selected expert is notified. The expert can then provide corresponding advice online.

[0443] A new feature related to virtual stores is the ability to receive expert advice, which is particularly useful in designing sales channels and developing promotional strategies. For example, businesses can get specific advice from experts on how to design new digital sales channels for their specialty products.

[0444] In this invention, a practical example is to consider a case where an agricultural business operator wants to improve their sales strategy on an online marketplace. In this case, the business operator inputs the problem from a terminal. Next, the generating AI model generates optimal advice using prompt sentences such as, "Please propose a promotional strategy for a local agricultural business operator to effectively run a large-scale social media campaign. We are especially looking for creative ideas that will help in developing new markets." This enables local businesses to effectively utilize urban expertise and develop new markets.

[0445] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0446] Step 1:

[0447] Users access the platform using their devices and enter their registration information and consultation details. The entered registration information and consultation details are sent to the server in digital format. This generates a data stream based on the consultation content.

[0448] Step 2:

[0449] The server receives the data stream and stores the registration information in a data structure. The consultation content is also passed to a generative AI model, which analyzes the business background and challenges through natural language processing. This analysis identifies the necessary expertise and support needs.

[0450] Step 3:

[0451] The analysis results from the generated AI model are input into the matching algorithm. The server compares the analysis results with expert engineer information in the database and automatically selects the most suitable engineer. This process obtains the expert engineer's identification data.

[0452] Step 4:

[0453] The server sends a notification to the selected specialist technician. The notification includes the details of the business's consultation and the support required, providing the technician with the basic information needed to prepare specific advice.

[0454] Step 5:

[0455] Technicians provide advice to clients via their devices. The advice is sent to the service provider in digital format and displayed on the user's device. This allows the service provider to receive feedback from expert technicians.

[0456] Step 6:

[0457] Based on the advice received, users provide feedback from their devices. This feedback is sent to the server and stored in a data structure. This information is used for future analysis and system improvements.

[0458] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0459] This invention incorporates an emotion engine that recognizes user emotions into an online platform that efficiently matches local businesses with urban-based professional technicians. The system begins with the server receiving registration information of businesses and technicians from terminals and storing it in a database. Subsequently, when businesses input consultation details using the terminal, the emotion engine analyzes the input data and evaluates the user's emotional state.

[0460] The server analyzes inquiries from businesses using a generation AI model to identify the business background, challenges, and required skills. This analysis, along with emotional information obtained from an emotion engine, is used to design a screen interface that minimizes user stress.

[0461] Furthermore, based on these analysis results, the server automatically selects the most suitable expert technician. In the selection process, emotional data obtained by the emotion engine is used as a factor in determining which technician will provide the appropriate response. In particular, if the user is experiencing stress, a technician with the ability to help them relax may be selected.

[0462] Using a notification function, the server sends client information to a selected technician, who then provides advice to the service provider via their terminal. The emotion engine constantly monitors the emotional states of both parties during the conversation and suggests adjusting the support content as needed.

[0463] As a concrete example, consider a manufacturing company considering the introduction of a new production technology. When the company inputs its inquiry, the emotion engine recognizes that the company is feeling anxious. Based on the generated AI model and emotion data, the server selects an experienced and calm engineer to provide support to reassure the company. In this way, more effective business support is achieved by taking the user's emotions into consideration.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] Users register on the online platform using their devices. Users enter their basic information, areas of expertise, and areas of interest, and the server receives this information and stores it in a database.

[0467] Step 2:

[0468] Users input their needs through their device, specifically describing the type of support they require. The server immediately sends the entered text to an emotion engine to analyze the user's emotions. This analysis can then measure, for example, levels of anxiety and stress.

[0469] Step 3:

[0470] The server uses a generative AI model to analyze the consultation content in detail. This analysis identifies the business background, the challenges being faced, and the necessary skills. Emotional data from the emotion engine is also taken into consideration to prepare the server to provide the most appropriate support for the user's situation.

[0471] Step 4:

[0472] The server automatically selects the most suitable expert from the database based on the analysis results. This selection process evaluates the expert's skills and the user's emotional state. If the user is experiencing anxiety, an expert with skills in relaxation will be selected.

[0473] Step 5:

[0474] The server promptly sends a notification to the selected technician. The technician uses their terminal to prepare for the user's consultation based on detailed consultation content and emotional information.

[0475] Step 6:

[0476] Engineers contact users via terminals and provide specific advice through online meetings and chats. An emotion engine constantly monitors the two-way conversation to ensure effective support tailored to the user's emotions.

[0477] Step 7:

[0478] After receiving advice, users input feedback on their impressions and the effects via their terminal. The server records this feedback in a database and uses it for future system improvements and evaluation of engineers.

[0479] (Example 2)

[0480] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0481] This solution addresses the problem of delays in business problem-solving due to the inability of local providers to effectively match with urban experts who possess the necessary specialized knowledge. It also addresses the issue of reduced effectiveness of dialogue due to the disregard of the client's emotional state.

[0482] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0483] In this invention, the server includes means for receiving registration information of providers and experts and storing it in an information aggregation device, means for analyzing the content of consultations from providers through a generating AI model, and means for matching the most suitable expert based on emotional data obtained by an emotion analysis device. This makes it possible to appropriately match the necessary expert while taking into account the emotions of the person seeking advice, thereby enabling effective problem solving.

[0484] A "provider" refers to a local organization or individual that provides services or goods.

[0485] A "specialist" is an individual or group located in an urban area who possesses advanced knowledge or skills in a specific field.

[0486] An "online platform" is online software that connects providers and experts via the internet.

[0487] An "information aggregation device" refers to a database or storage device that stores and manages registration information of providers and experts.

[0488] A "generative AI model" is a model built with artificial intelligence that analyzes input data to identify the background, problems, and required capabilities of a task.

[0489] An "emotion analysis device" is a system that analyzes the input data of a provider and evaluates their emotional state.

[0490] "Matching method" refers to the process or system for selecting the most suitable expert based on the generated AI model and sentiment analysis results.

[0491] The following describes embodiments for carrying out the present invention. This system forms an online platform that efficiently matches local providers with urban experts.

[0492] The server receives registration information of providers and experts from terminals via the internet and stores it in an information aggregation device. Database technology is used in this process, and the information is managed securely. When a user inputs their consultation details using a terminal, a generative AI model running on the server analyzes the input data. Specifically, it utilizes natural language processing technology to identify the background of the work, the problem, and the required skills. In this process, an emotion analysis device also judges the emotional aspects of the input data and evaluates the user's emotional state.

[0493] Based on the analyzed data, the server performs a matching process to select the most suitable expert. Information is sent to the selected expert via a notification system, and the expert can use their terminal to provide advice to the provider.

[0494] As a concrete example, consider a case where a manufacturer seeks advice regarding the introduction of a new technology. Suppose the user enters the prompt message "I have concerns about the new manufacturing process" into the terminal. In this case, the generative AI model detects the provider's sources of anxiety, and the emotion analysis device evaluates their emotional tendencies. The server then selects a calm expert with relaxation skills and has them provide expert advice, thereby reassuring the provider.

[0495] In this way, users can engage in effective and stress-free interactions with experts who possess a high level of specialized knowledge.

[0496] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0497] Step 1:

[0498] Users enter their registration information as providers or professionals using a terminal. This data includes basic information such as name, contact information, skills, and experience. The terminal sends this data to a server, which stores it in an information aggregation device once the user has completed the entry. A database system is used for this storage process, ensuring security and quick access.

[0499] Step 2:

[0500] The provider inputs the consultation details via a terminal. Once the consultation details are entered in prompt format, the terminal sends the data to the server. The server inputs the received consultation details into a generating AI model and performs analysis to identify the background of the work, the problem, and the required skills. Natural language processing technology is used here, and the analyzed information is stored on the server as output.

[0501] Step 3:

[0502] In parallel, the server uses an emotion analysis device to perform an emotional analysis of the input consultation content. This uses an algorithm that evaluates the emotional elements of the text, and the user's emotional state is estimated. The result of the emotional evaluation is obtained as output and is also stored within the server.

[0503] Step 4:

[0504] Based on the analysis results and sentiment evaluation results from the generative AI model, the server executes a matching algorithm to select the most suitable expert. The input consists of both analysis information and sentiment evaluation, and considering these, it outputs a list of the most appropriate experts from among multiple candidates.

[0505] Step 5:

[0506] The server sends information to selected experts via a notification system. This information includes details about the provider's consultation topic and the required skills. Upon receiving the notification, experts use their devices to prepare to provide advice to the provider.

[0507] Step 6:

[0508] During conversations with experts via the terminal, the emotion analysis device continuously monitors the user's emotional state. In response to changes in the emotional state, the server provides real-time feedback to the expert, suggesting adjustments to the support provided to enhance the effectiveness of the conversation. The ultimate output is an improved quality of conversation.

[0509] (Application Example 2)

[0510] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] In today's industrial environment, field workers face the challenge of maintaining and troubleshooting complex machinery, requiring appropriate and timely technical support. However, providing nuanced support tailored to each worker's skill level and emotional state is difficult, potentially impacting work efficiency and accuracy. Against this backdrop, there is a need for a technical support platform that takes workers' emotional states into account.

[0512] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0513] In this invention, the server includes means for receiving registration information of business organizations and professional personnel and storing it in a data storage unit; means including an emotion analysis engine that recognizes the emotional state of workers and provides appropriate technical support according to that state; and means for generating simpler work instructions and technical support based on the emotional state of workers. This makes it possible for workers to receive optimal technical support quickly while reducing their emotional burden.

[0514] A "business organization" is an entity organized to perform a specific business or commercial activity.

[0515] A "specialized professional" is an individual who possesses a high level of expertise and skills in a specific field.

[0516] An "online infrastructure" is a system with a platform structure that can be accessed via the internet.

[0517] A "data storage unit" is a recording medium or technical means for centrally storing and managing information.

[0518] A "data generation model" is an algorithm or system that analyzes input information and generates new information or knowledge.

[0519] "Verification means" refers to a method or apparatus for comparing different pieces of information and determining their compatibility or relationship.

[0520] An "emotion analysis engine" is a program or device for identifying and evaluating a person's emotions.

[0521] "Technical support" refers to services that provide expertise and assistance with the operation and maintenance of machinery and equipment.

[0522] This system is designed to efficiently match local businesses with urban professionals online. The server receives registration information from businesses and professionals and stores it in a data aggregation unit. This provides a foundation for users to access the system at any time.

[0523] When a consultation request is entered into the server, it uses a data generation model to analyze the content and identify the background, problems, and required skills for the work. Using a generation AI model such as Google's TensorFlow enables rapid and accurate analysis. Based on the analysis results, the most suitable professional is automatically selected using matching methods. In this process, the emotional state of the person seeking advice is also considered using an emotion analysis engine.

[0524] The emotion analysis engine analyzes emotional data obtained from the user's smartphone camera and microphone to determine in real time whether the user is experiencing anxiety or stress. This allows for the provision of technical support tailored to the user's emotional state. Microsoft's Azure Cognitive Services could be used for emotion analysis.

[0525] For example, if a factory worker is struggling to troubleshoot a robot, the emotion analysis engine detects this, and a generative AI model generates a simple troubleshooting guide. Then, if necessary, the appropriate expert is automatically selected and contacted to provide remote technical assistance.

[0526] A concrete example of a prompt message is: "A robot malfunction has been detected, and the operator is feeling anxious. Please generate a guide to resolve the issue with simple steps." This prompt allows the system to generate a guide that provides appropriate assistance based on the situation.

[0527] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0528] Step 1:

[0529] The terminal receives registration information for business organizations and professional personnel from the input screen. The entered information is stored in a database based on each attribute (name, contact method, area of ​​expertise, etc.). This allows the server to quickly access and manage the relevant information.

[0530] Step 2:

[0531] The user inputs consultation information through their terminal. The server receives this information and sends it as a prompt to the generating AI model. The consultation content is treated as text data, and the generating AI model (using TensorFlow) analyzes the business background and problems to identify the necessary capabilities. The output is returned to the server as the analysis result.

[0532] Step 3:

[0533] The server uses an emotion analysis engine to analyze user emotion data collected from the device's camera and microphone. Using audio and video data as input, it determines the user's emotional state using Azure Cognitive Services. The output is emotional state information that allows the user to receive technical support with confidence.

[0534] Step 4:

[0535] The server selects the most suitable professional based on the analysis results from steps 2 and 3. Using business analysis results and emotional state information as input data, the server generates information on the selected professional as output using a matching mechanism. In this process, the different skill sets and emotional response capabilities of the professionals are taken into consideration.

[0536] Step 5:

[0537] Selected professionals receive immediate notifications via their terminals. These notifications include information about the organization's concerns and emotional state. Based on these notifications, the professionals provide appropriate advice to the organization to resolve the issues. The output is the advice given to the organization and its effectiveness.

[0538] Step 6:

[0539] The server collects feedback from the business organization. The feedback data received from terminals is processed for system improvement and used to enhance the accuracy of future analyses and selections. This feedback provides crucial information for optimizing the system.

[0540] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0541] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0542] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0543] [Fourth Embodiment]

[0544] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0545] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0546] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0547] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0548] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0549] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0550] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0551] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0552] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0553] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0554] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0555] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0556] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0557] This invention provides an online platform that enables local businesses to receive knowledge and support from urban-based technical experts. Users access the platform using a terminal and register by entering their information. The registration information is stored in a database by the server. This allows for the management of profiles for both businesses and technical experts.

[0558] When entering the consultation details, the user inputs specific challenges and necessary support related to their business using their device. The server receives this information, formats it appropriately, and saves it. This consultation content is then analyzed by a generative AI model to identify the business background, the challenges it faces, and the required expertise.

[0559] The server performs appropriate matching based on the analysis results and the information of engineers managed in the database. The engineer who best matches the requirements is selected, and a notification is sent to that engineer. This notification allows the engineer to access the service provider in need of support via their terminal and provide the necessary advice and information.

[0560] Furthermore, local businesses that receive advice provide feedback via their terminals, and the server stores this feedback in a database. This feedback is used to improve the system and is also used as part of a process to pass that feedback back to engineers and other businesses.

[0561] As a concrete example, consider a case where a local agricultural business is looking for a new sales strategy. The business inputs its challenges using a terminal, and a server analyzes the situation and selects a specialist in sales strategies. This specialist advises the business on specific sales channels and marketing methods, supporting the agricultural business in developing new markets. In this way, a system is created that allows local businesses to leverage urban expertise for actual business expansion.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] The user uses their device to access the online platform's registration page and enters information such as their name, contact information, area of ​​expertise, and required support. The server receives the registration information submitted by the user and stores it in its database.

[0565] Step 2:

[0566] The user uses a terminal to input specific details of their inquiry. For example, they might describe in detail that they "need a new marketing strategy." The server receives the input inquiry, formats it appropriately, and saves it to the database.

[0567] Step 3:

[0568] The server uses a generative AI model to analyze the stored consultation content. This analysis identifies the background, issues, and required skills of the consultation. The generative AI model derives conclusions such as "the development of a marketing strategy is needed."

[0569] Step 4:

[0570] The server matches the information of engineers in the database based on the analysis results of the generated AI model. Using a matching algorithm, it automatically selects the most suitable engineer. For example, it might identify an engineer with expertise in marketing.

[0571] Step 5:

[0572] The server sends a notification to the selected technician. The technician accesses the platform from their terminal and prepares to respond to the designated service provider.

[0573] Step 6:

[0574] Technicians use terminals to contact businesses and provide specific advice. This includes information exchange via video calls and messaging systems. Users (businesses) then use this information to adjust and improve their businesses.

[0575] Step 7:

[0576] Users (businesses) input feedback via a terminal after receiving advice. The server receives this feedback and stores it in a database. The feedback information is used to improve the system and evaluate engineers.

[0577] (Example 1)

[0578] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0579] Businesses in rural areas face the challenge of not being able to quickly and effectively receive knowledge and support from specialists concentrated in urban areas. In particular, the difficulty in accessing appropriate specialists and accurately understanding the nature of their consultations leads to delays in the information and advice necessary for business development.

[0580] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0581] In this invention, the server includes means for receiving registration information of businesses and expert technicians and storing it in an information storage means, means for receiving and inputting consultation content from businesses and analyzing it using a generated AI model, and means for automatically selecting and coordinating a suitable expert technician based on the analysis results. This makes it possible for local businesses to quickly receive expert advice from the most suitable technician.

[0582] "Information storage means" refers to a device or system that has the function of receiving and storing data, including registration information of businesses and technical experts.

[0583] A "generative AI model" is an artificial intelligence algorithm that analyzes input data to identify the background and challenges of a business.

[0584] "Adjustment means" refers to a device or system that has the function of selecting a suitable expert based on the results analyzed by a generative AI model and automatically matching them with a business operator.

[0585] A "means of collecting opinions" refers to a system for receiving, storing, and using feedback and opinions from those seeking advice to improve the system later on.

[0586] The "notification function" is a communication method used to inform the system-selected expert technicians about the timing and content of advice to be provided.

[0587] This invention relates to the construction of an online platform that enables local businesses to receive appropriate support from expert technicians. The server uses information storage means to store registration information received from businesses and expert technicians in a database. This database manages the profiles of businesses and technicians and provides the basis for matching them.

[0588] Users access the platform using their devices and input their inquiry details. This includes information about specific business challenges and the support they need. The server receives this input and analyzes the data using a generative AI model. The generative AI model understands the inquiry and uses prompts to identify the business context, the challenges faced, and the technologies required.

[0589] Based on the analyzed data, the server automatically selects the appropriate expert technician and notifies the service provider of the selection result through a coordination mechanism. The technician receives the notification and can then provide advice directly to the service provider via a terminal. The service provider also inputs feedback on the advice received via the terminal, and the server stores this information in a data storage system. This feedback is used to improve the system and facilitate future support.

[0590] As a concrete example, consider a case where a local agricultural business is exploring new sales strategies. The business inputs its market development challenges into the platform via a terminal. The server performs analysis and automatically selects a technician with expertise in sales strategies. The selected technician proposes effective marketing methods to the agricultural business and helps it enter new markets. This allows the business to expand its business by leveraging advanced knowledge from urban areas.

[0591] An example of a prompt might be a question like, "What marketing methods would be effective for agricultural businesses exploring new sales strategies?"

[0592] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0593] Step 1:

[0594] Users access the online platform using their devices and enter the necessary personal and business information. Once the data entered by the user is transmitted, the server receives this information and stores it in a database using an information storage mechanism. This registers the user's profile in the system.

[0595] Step 2:

[0596] Users use a terminal to enter detailed information about their business needs. For example, they might enter questions about specific technical issues or business strategies. This information is organized into prompts and sent to the server. The server receives this information and records it in its database.

[0597] Step 3:

[0598] The server passes the consultation details stored in the database to the generating AI model. The generating AI model analyzes the input data and performs data calculations to identify the business background, challenges, and required expertise. The analysis results are returned to the server.

[0599] Step 4:

[0600] The server compares the analysis results obtained by the generated AI model with the information of engineers in the database. Using this information, the server executes a process to select the most suitable expert engineer. The server then verifies the profile of the selected engineer.

[0601] Step 5:

[0602] The server uses a notification function to send a message to the selected technician requesting their advice on the matter. Upon receiving the notification, the technician contacts the service provider via their terminal and prepares to provide specific advice and technical support.

[0603] Step 6:

[0604] After receiving advice from a technician, the user enters feedback on the content into their terminal. This feedback data is collected by the server and stored in an information storage system. This data is used for system improvement and sharing with other users.

[0605] (Application Example 1)

[0606] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0607] Local businesses often have limited access to specialized technologies and services available in urban areas. As a result, they may not receive sufficient support, particularly in designing new sales channels and developing promotional strategies utilizing virtual stores, which can make business expansion and market development difficult.

[0608] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0609] This invention includes a server that receives registration information of businesses and expert technicians and stores it in a data structure; a means that receives input of consultation content from businesses and includes a generative AI model that analyzes said consultation content; a matching means that automatically selects the most suitable expert technician; and a means that allows businesses to receive advice from experts on virtual sales channel design or promotion strategies. This enables local businesses to receive the necessary support from expert technicians and expand their businesses through the effective use of virtual stores.

[0610] An "online platform" is a system that connects local businesses with urban-based technical experts via the internet.

[0611] A "data structure" is a formatted information body used to organize and store registration information of businesses and technical experts.

[0612] A "generative AI model" is a model that uses artificial intelligence technology to analyze the content of inquiries from businesses and identify the background, challenges, and necessary skills.

[0613] The "matching method" is a function that automatically selects the most suitable expert for a business based on the analysis results of the generated AI model.

[0614] "Virtual sales channel design or promotion strategy" is a concept that uses digital technology to build sales channels and advertising methods for products and services in a virtual space.

[0615] This invention provides an online platform for efficiently matching local businesses with urban-based technical experts. The main processing is performed on a server. Specific embodiments are described below.

[0616] The hardware can include smartphones, smart glasses, or head-mounted displays. These devices are used as terminals to allow users (businesses) to access the platform. Registration information and consultation details are sent from the terminal to the server. The server receives this data and stores it in a data structure.

[0617] The AI ​​model installed on the server analyzes the inquiries received from businesses to identify the business background, challenges, and required skills. Based on this analysis, the most suitable expert is automatically selected, and the selected expert is notified. The expert can then provide corresponding advice online.

[0618] A new feature related to virtual stores is the ability to receive expert advice, which is particularly useful in designing sales channels and developing promotional strategies. For example, businesses can get specific advice from experts on how to design new digital sales channels for their specialty products.

[0619] In this invention, a practical example is to consider a case where an agricultural business operator wants to improve their sales strategy on an online marketplace. In this case, the business operator inputs the problem from a terminal. Next, the generating AI model generates optimal advice using prompt sentences such as, "Please propose a promotional strategy for a local agricultural business operator to effectively run a large-scale social media campaign. We are especially looking for creative ideas that will help in developing new markets." This enables local businesses to effectively utilize urban expertise and develop new markets.

[0620] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0621] Step 1:

[0622] Users access the platform using their devices and enter their registration information and consultation details. The entered registration information and consultation details are sent to the server in digital format. This generates a data stream based on the consultation content.

[0623] Step 2:

[0624] The server receives the data stream and stores the registration information in a data structure. The consultation content is also passed to a generative AI model, which analyzes the business background and challenges through natural language processing. This analysis identifies the necessary expertise and support needs.

[0625] Step 3:

[0626] The analysis results from the generated AI model are input into the matching algorithm. The server compares the analysis results with expert engineer information in the database and automatically selects the most suitable engineer. This process obtains the expert engineer's identification data.

[0627] Step 4:

[0628] The server sends a notification to the selected specialist technician. The notification includes the details of the business's consultation and the support required, providing the technician with the basic information needed to prepare specific advice.

[0629] Step 5:

[0630] Technicians provide advice to clients via their devices. The advice is sent to the service provider in digital format and displayed on the user's device. This allows the service provider to receive feedback from expert technicians.

[0631] Step 6:

[0632] Based on the advice received, users provide feedback from their devices. This feedback is sent to the server and stored in a data structure. This information is used for future analysis and system improvements.

[0633] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0634] This invention incorporates an emotion engine that recognizes user emotions into an online platform that efficiently matches local businesses with urban-based professional technicians. The system begins with the server receiving registration information of businesses and technicians from terminals and storing it in a database. Subsequently, when businesses input consultation details using the terminal, the emotion engine analyzes the input data and evaluates the user's emotional state.

[0635] The server analyzes inquiries from businesses using a generation AI model to identify the business background, challenges, and required skills. This analysis, along with emotional information obtained from an emotion engine, is used to design a screen interface that minimizes user stress.

[0636] Furthermore, based on these analysis results, the server automatically selects the most suitable expert technician. In the selection process, emotional data obtained by the emotion engine is used as a factor in determining which technician will provide the appropriate response. In particular, if the user is experiencing stress, a technician with the ability to help them relax may be selected.

[0637] Using a notification function, the server sends client information to a selected technician, who then provides advice to the service provider via their terminal. The emotion engine constantly monitors the emotional states of both parties during the conversation and suggests adjusting the support content as needed.

[0638] As a concrete example, consider a manufacturing company considering the introduction of a new production technology. When the company inputs its inquiry, the emotion engine recognizes that the company is feeling anxious. Based on the generated AI model and emotion data, the server selects an experienced and calm engineer to provide support to reassure the company. In this way, more effective business support is achieved by taking the user's emotions into consideration.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] Users register on the online platform using their devices. Users enter their basic information, areas of expertise, and areas of interest, and the server receives this information and stores it in a database.

[0642] Step 2:

[0643] Users input their needs through their device, specifically describing the type of support they require. The server immediately sends the entered text to an emotion engine to analyze the user's emotions. This analysis can then measure, for example, levels of anxiety and stress.

[0644] Step 3:

[0645] The server uses a generative AI model to analyze the consultation content in detail. This analysis identifies the business background, the challenges being faced, and the necessary skills. Emotional data from the emotion engine is also taken into consideration to prepare the server to provide the most appropriate support for the user's situation.

[0646] Step 4:

[0647] The server automatically selects the most suitable expert from the database based on the analysis results. This selection process evaluates the expert's skills and the user's emotional state. If the user is experiencing anxiety, an expert with skills in relaxation will be selected.

[0648] Step 5:

[0649] The server promptly sends a notification to the selected technician. The technician uses their terminal to prepare for the user's consultation based on detailed consultation content and emotional information.

[0650] Step 6:

[0651] Engineers contact users via terminals and provide specific advice through online meetings and chats. An emotion engine constantly monitors the two-way conversation to ensure effective support tailored to the user's emotions.

[0652] Step 7:

[0653] After receiving advice, users input feedback on their impressions and the effects via their terminal. The server records this feedback in a database and uses it for future system improvements and evaluation of engineers.

[0654] (Example 2)

[0655] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] This solution addresses the problem of delays in business problem-solving due to the inability of local providers to effectively match with urban experts who possess the necessary specialized knowledge. It also addresses the issue of reduced effectiveness of dialogue due to the disregard of the client's emotional state.

[0657] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0658] In this invention, the server includes means for receiving registration information of providers and experts and storing it in an information aggregation device, means for analyzing the content of consultations from providers through a generating AI model, and means for matching the most suitable expert based on emotional data obtained by an emotion analysis device. This makes it possible to appropriately match the necessary expert while taking into account the emotions of the person seeking advice, thereby enabling effective problem solving.

[0659] A "provider" refers to a local organization or individual that provides services or goods.

[0660] A "specialist" is an individual or group located in an urban area who possesses advanced knowledge or skills in a specific field.

[0661] An "online platform" is online software that connects providers and experts via the internet.

[0662] An "information aggregation device" refers to a database or storage device that stores and manages registration information of providers and experts.

[0663] A "generative AI model" is a model built with artificial intelligence that analyzes input data to identify the background, problems, and required capabilities of a task.

[0664] An "emotion analysis device" is a system that analyzes the input data of a provider and evaluates their emotional state.

[0665] "Matching method" refers to the process or system for selecting the most suitable expert based on the generated AI model and sentiment analysis results.

[0666] The following describes embodiments for carrying out the present invention. This system forms an online platform that efficiently matches local providers with urban experts.

[0667] The server receives registration information of providers and experts from terminals via the internet and stores it in an information aggregation device. Database technology is used in this process, and the information is managed securely. When a user inputs their consultation details using a terminal, a generative AI model running on the server analyzes the input data. Specifically, it utilizes natural language processing technology to identify the background of the work, the problem, and the required skills. In this process, an emotion analysis device also judges the emotional aspects of the input data and evaluates the user's emotional state.

[0668] Based on the analyzed data, the server performs a matching process to select the most suitable expert. Information is sent to the selected expert via a notification system, and the expert can use their terminal to provide advice to the provider.

[0669] As a concrete example, consider a case where a manufacturer seeks advice regarding the introduction of a new technology. Suppose the user enters the prompt message "I have concerns about the new manufacturing process" into the terminal. In this case, the generative AI model detects the provider's sources of anxiety, and the emotion analysis device evaluates their emotional tendencies. The server then selects a calm expert with relaxation skills and has them provide expert advice, thereby reassuring the provider.

[0670] In this way, users can engage in effective and stress-free interactions with experts who possess a high level of specialized knowledge.

[0671] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0672] Step 1:

[0673] Users enter their registration information as providers or professionals using a terminal. This data includes basic information such as name, contact information, skills, and experience. The terminal sends this data to a server, which stores it in an information aggregation device once the user has completed the entry. A database system is used for this storage process, ensuring security and quick access.

[0674] Step 2:

[0675] The provider inputs the consultation details via a terminal. Once the consultation details are entered in prompt format, the terminal sends the data to the server. The server inputs the received consultation details into a generating AI model and performs analysis to identify the background of the work, the problem, and the required skills. Natural language processing technology is used here, and the analyzed information is stored on the server as output.

[0676] Step 3:

[0677] In parallel, the server uses an emotion analysis device to perform an emotional analysis of the input consultation content. This uses an algorithm that evaluates the emotional elements of the text, and the user's emotional state is estimated. The result of the emotional evaluation is obtained as output and is also stored within the server.

[0678] Step 4:

[0679] Based on the analysis results and sentiment evaluation results from the generative AI model, the server executes a matching algorithm to select the most suitable expert. The input consists of both analysis information and sentiment evaluation, and considering these, it outputs a list of the most appropriate experts from among multiple candidates.

[0680] Step 5:

[0681] The server sends information to selected experts via a notification system. This information includes details about the provider's consultation topic and the required skills. Upon receiving the notification, experts use their devices to prepare to provide advice to the provider.

[0682] Step 6:

[0683] During conversations with experts via the terminal, the emotion analysis device continuously monitors the user's emotional state. In response to changes in the emotional state, the server provides real-time feedback to the expert, suggesting adjustments to the support provided to enhance the effectiveness of the conversation. The ultimate output is an improved quality of conversation.

[0684] (Application Example 2)

[0685] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0686] In today's industrial environment, field workers face the challenge of maintaining and troubleshooting complex machinery, requiring appropriate and timely technical support. However, providing nuanced support tailored to each worker's skill level and emotional state is difficult, potentially impacting work efficiency and accuracy. Against this backdrop, there is a need for a technical support platform that takes workers' emotional states into account.

[0687] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0688] In this invention, the server includes means for receiving registration information of business organizations and professional personnel and storing it in a data storage unit; means including an emotion analysis engine that recognizes the emotional state of workers and provides appropriate technical support according to that state; and means for generating simpler work instructions and technical support based on the emotional state of workers. This makes it possible for workers to receive optimal technical support quickly while reducing their emotional burden.

[0689] A "business organization" is an entity organized to perform a specific business or commercial activity.

[0690] A "specialized professional" is an individual who possesses a high level of expertise and skills in a specific field.

[0691] An "online infrastructure" is a system with a platform structure that can be accessed via the internet.

[0692] A "data storage unit" is a recording medium or technical means for centrally storing and managing information.

[0693] A "data generation model" is an algorithm or system that analyzes input information and generates new information or knowledge.

[0694] "Verification means" refers to a method or apparatus for comparing different pieces of information and determining their compatibility or relationship.

[0695] An "emotion analysis engine" is a program or device for identifying and evaluating a person's emotions.

[0696] "Technical support" refers to services that provide expertise and assistance with the operation and maintenance of machinery and equipment.

[0697] This system is designed to efficiently match local businesses with urban professionals online. The server receives registration information from businesses and professionals and stores it in a data aggregation unit. This provides a foundation for users to access the system at any time.

[0698] When a consultation request is entered into the server, it uses a data generation model to analyze the content and identify the background, problems, and required skills for the work. Using a generation AI model such as Google's TensorFlow enables rapid and accurate analysis. Based on the analysis results, the most suitable professional is automatically selected using matching methods. In this process, the emotional state of the person seeking advice is also considered using an emotion analysis engine.

[0699] The emotion analysis engine analyzes emotional data obtained from the user's smartphone camera and microphone to determine in real time whether the user is experiencing anxiety or stress. This allows for the provision of technical support tailored to the user's emotional state. Microsoft's Azure Cognitive Services could be used for emotion analysis.

[0700] For example, if a factory worker is struggling to troubleshoot a robot, the emotion analysis engine detects this, and a generative AI model generates a simple troubleshooting guide. Then, if necessary, the appropriate expert is automatically selected and contacted to provide remote technical assistance.

[0701] A concrete example of a prompt message is: "A robot malfunction has been detected, and the operator is feeling anxious. Please generate a guide to resolve the issue with simple steps." This prompt allows the system to generate a guide that provides appropriate assistance based on the situation.

[0702] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0703] Step 1:

[0704] The terminal receives registration information for business organizations and professional personnel from the input screen. The entered information is stored in a database based on each attribute (name, contact method, area of ​​expertise, etc.). This allows the server to quickly access and manage the relevant information.

[0705] Step 2:

[0706] The user inputs consultation information through their terminal. The server receives this information and sends it as a prompt to the generating AI model. The consultation content is treated as text data, and the generating AI model (using TensorFlow) analyzes the business background and problems to identify the necessary capabilities. The output is returned to the server as the analysis result.

[0707] Step 3:

[0708] The server uses an emotion analysis engine to analyze user emotion data collected from the device's camera and microphone. Using audio and video data as input, it determines the user's emotional state using Azure Cognitive Services. The output is emotional state information that allows the user to receive technical support with confidence.

[0709] Step 4:

[0710] The server selects the most suitable professional based on the analysis results from steps 2 and 3. Using business analysis results and emotional state information as input data, the server generates information on the selected professional as output using a matching mechanism. In this process, the different skill sets and emotional response capabilities of the professionals are taken into consideration.

[0711] Step 5:

[0712] Selected professionals receive immediate notifications via their terminals. These notifications include information about the organization's concerns and emotional state. Based on these notifications, the professionals provide appropriate advice to the organization to resolve the issues. The output is the advice given to the organization and its effectiveness.

[0713] Step 6:

[0714] The server collects feedback from the business organization. The feedback data received from terminals is processed for system improvement and used to enhance the accuracy of future analyses and selections. This feedback provides crucial information for optimizing the system.

[0715] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0716] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0717] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0718] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0719] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0720] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0721] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0722] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0723] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0724] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0725] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0726] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0727] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0728] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0729] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0730] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0731] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0732] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0733] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0734] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0735] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0736] The following is further disclosed regarding the embodiments described above.

[0737] (Claim 1)

[0738] An online platform for matching businesses in rural areas with specialized engineers concentrated in urban areas,

[0739] A means of receiving and storing registration information of businesses and technical experts in a database,

[0740] A means including a generative AI model that receives input of consultation content from businesses and analyzes said consultation content,

[0741] A matching means that automatically selects the most suitable expert based on the analysis results of the generated AI model,

[0742] A means to support the selected expert technician in providing advice to the client,

[0743] A means of collecting feedback from clients and using it to improve the system,

[0744] A system that includes this.

[0745] (Claim 2)

[0746] The system according to claim 1, comprising the step of a generating AI model identifying the business background, challenges, and required skills.

[0747] (Claim 3)

[0748] The system according to claim 1, which has a function to send notifications to expert technicians and includes means by which said technicians provide advice to local businesses.

[0749] "Example 1"

[0750] (Claim 1)

[0751] A means for receiving registration information of businesses and technical experts and storing it in an information storage means,

[0752] A means of receiving and inputting consultation content from businesses and using a generative AI model to analyze said consultation content,

[0753] A means for automatically selecting a suitable expert based on the analysis results of the generated AI model,

[0754] Means to support the selected expert technician in providing advice to the client,

[0755] The means of collecting opinions from clients and using them to improve the construction,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, comprising the step of a generating AI model identifying the background, challenges, and required skills of a task.

[0759] (Claim 3)

[0760] The system according to claim 1, which has a function to send notifications to expert technicians and includes means by which said technicians provide advice to local businesses.

[0761] "Application Example 1"

[0762] (Claim 1)

[0763] An online platform for matching businesses in rural areas with specialized engineers concentrated in urban areas,

[0764] A means for receiving registration information of businesses and technical experts and storing it in a data structure,

[0765] A means including a generative AI model that receives input of consultation content from businesses and analyzes said consultation content,

[0766] A matching means that automatically selects the most suitable expert based on the analysis results of the generated AI model,

[0767] A means to support the selected expert technician in providing advice to the client,

[0768] A means of collecting feedback from clients and using it to improve the system,

[0769] A means to receive expert advice on virtual sales channel design or promotional strategies,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, which includes steps to help design and promote a virtual store, in addition to a step to identify the business background, challenges, and required skills, using a generative AI model.

[0773] (Claim 3)

[0774] The system according to claim 1, which has a function to send notifications to expert technicians, and includes means by which said technicians provide advice to local businesses in a virtual space.

[0775] "Example 2 of combining an emotion engine"

[0776] (Claim 1)

[0777] An online platform for matching providers in rural areas with experts concentrated in urban areas,

[0778] Means for receiving registration information of providers and experts and storing it in an information aggregation device,

[0779] A means including a generative AI model that receives input of consultation content from a provider and analyzes said consultation content,

[0780] A matching means that automatically selects the most suitable expert based on the analysis results and emotion analysis results from the generated AI model,

[0781] Means to support the selected expert in providing advice to the client,

[0782] A means by which an emotion analysis device monitors the emotional state during a conversation and makes suggestions to adjust the support provided,

[0783] The means of collecting feedback from clients and using it to improve the system,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, comprising the step of a generating AI model identifying the background, problem, and required capabilities of the business.

[0787] (Claim 3)

[0788] The system according to claim 1, comprising a function for transmitting information to experts, and means by which the experts provide advice to local providers.

[0789] "Application example 2 when combining with an emotional engine"

[0790] (Claim 1)

[0791] An online platform for matching business organizations in rural areas with professional personnel concentrated in urban areas,

[0792] A means for receiving registration information of business organizations and professional personnel and storing it in a data aggregation unit,

[0793] A means including a data generation model that inputs and receives consultation information from business organizations and analyzes said consultation information,

[0794] A matching means that automatically selects the most suitable expert based on the analysis results of the data generation model,

[0795] Means to support the selected professional personnel in providing advice to the client,

[0796] The means of collecting feedback from clients and using it to improve the system,

[0797] A means including an emotion analysis engine that recognizes the emotional state of a worker and provides appropriate technical support according to that state,

[0798] A means of generating simpler work instructions and technical support based on the emotional state of the worker,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the data generation model includes the step of identifying the business context, problems, and required capabilities.

[0802] (Claim 3)

[0803] The system according to claim 1, comprising a function to send notifications to expert personnel, and means by which such personnel provide advice to local business organizations. [Explanation of Symbols]

[0804] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An online platform for matching businesses in rural areas with specialized engineers concentrated in urban areas, A means of receiving and storing registration information of businesses and technical experts in a database, A means including a generative AI model that receives input of consultation content from businesses and analyzes said consultation content, A matching means that automatically selects the most suitable expert based on the analysis results of the generated AI model, A means to support the selected expert technician in providing advice to the client, A means of collecting feedback from clients and using it to improve the system, A system that includes this.

2. The system according to claim 1, comprising the step of a generating AI model identifying the business background, challenges, and required skills.

3. The system according to claim 1, which has a function to send notifications to expert technicians and includes means by which said technicians provide advice to local businesses.

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